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ACEEE Int. J. on Communication, Vol. 02, No. 03 , Nov 20 1 1 

Towards a Software Framework for Automatic 
Business Process Redesign 

Marwa M.Essam 1 , Selma Limam Mansar 2 

1 Faculty of Information and Computer Sciences -Ain Shams University/Cairo, Egypt 


2 Carnegie Mellon University-Qatar /Information Systems Program, Doha, Qatar 


Abstract — A key element to the success of any organization is 
the ability to continuously improve its business process 
performance. Efficient Business Process Redesign (BPR) 
methodologies are needed to allow organizations to face the 
changing business conditions. For a long time, practices for 
BPR were done case-by-case and were based on the insights 
and knowledge of an expert to the organization. It can be 
argued that efficiency, however, can further be achieved with 
the support of automatic process redesign tools which are few 
at the moment. Process mining as a recent approach allows 
for the extraction of information from event logs recorded in 
different information systems. In this paper we argue that 
results driven by process mining techniques can be used to 
capture the various types of inefficiencies in the organization 
and hence propose efficient redesigns of its business model. 
We first give an outline on the current directions towards 
automatic BPR followed by a review on the different process 
mining techniques and its usage in different applications. 
Then, a specific framework of a Software tool that uses process 
mining to support automatic BPR is presented. 

Index Terms — Process Mining, Business Process Redesign, 
Business Process Management 

I. Introduction 

A business process is a collection of related, structured 
activities that produce a service or product that meets the 
needs of a client. Business processes are critical to any 
organization as they generate revenue and often represent a 
significant proportion of costs. Nowadays, many Business 
Process Management (BPM) systems exist in the market (Ex. : 
FileNet and Ultimas). BPM systems provide organizations 
with a broad range of facilities to design, enact, control and 
analyze their business process [1]. A list of the some of the 
cross-industry BPM suites with their relative strengths can 
be found in [2] . 

Despite its popularity and obvious pay-offs, the current 
practices for monitoring and analyzing the execution of BPM 
systems in the organizational reality still leaves a lot to be 
desired [3]. There is a vital need for BPM systems to 
satisfactory support Business Process Redesign (BPR). In 
many cases, the developed business functions do not 
effectively reflect the actual business process. Many of the 
implemented business functions are never used. Other 
business functions provide more functionality than actually 
needed. Another issue is related to the evolution of business 
processes and their variability. In many domains (ex: 
healthcare), frequent process changes requires the continuous 

©2011 ACEEE 

adaptation of the supporting IS. 

Currently, most BPR projects depend mainly on an expert 
to an organization. Experts/ Consultants may use some tools 
for process modeling, business planning or process 
prototyping [4]. However, there is currently no tool that 
supports the automatic redesign from the old business 
process to new innovative business processes. 

In recent years, process mining was introduced in the 
context of business process management [5] . Process Mining, 
similarly to data mining, allows for the extraction of 
information from event logs recorded in BPM-based systems. 
Some of the possibilities offered by process mining results 
are the discovery of new business process models, the 
checking of the conformance to some prescriptive or 
descriptive models, or the extension of an initial model with 
analysis data. 

In this paper we argue that an evolutionary redesign to 
business processes can be reached using results driven by 
process mining techniques. The evolutionary redesign is 
based on the application of general best practices or heuristic 
rules to an existing design. We think that realizing adaptations 
to business process has become a difficult task to accomplish 
due to the lack of knowledge to customize the process logic 
at a sufficient level. However, using process mining, different 
models can be extracted from the reality logs and various 
types of inefficiencies in the organization can be captured by 
analyzing these logs. These results can be used as an input 
base for a tool that suggests efficient redesigns to the 
business process, hence, providing consultants and experts 
with a vision on how to get from the old process to the new 

This paper is arranged as follows: Section II introduces 
the notion of modeling a business process with an example 
on a credit application process. Section III discusses some 
the related work towards converting old process designs to 
new designs. In Section IV we give a review on the different 
process mining algorithms and some of its application areas. 
In Section V, we discuss our vision on how process mining 
can be used in a software tool that supports the automatic 
redesign of business processes. 

II. Business Process Modeling 

A group of related tasks that together create value for a 
customer is called a business process. Different modeling 
languages/techniques can be used to represent different 


ACEEE Int. J. on Communication, Vol. 02, No. 03, Nov 201 1 



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Trans Far to 

Figure 1. Credit Application Process Model 

aspects of the business process. Control Flow modeling 
languages, like Petri-nets, represent the different activities in 
the process with constraints to control the execution between 
them (In what order activities are executed). Data models like 
Entity Relationship Diagrams represent the data organization 
in a process. Organizational models like organizational charts 
represent the structure in which the business process will 
execute (the entities/roles that can perform work for the 
enterprise). An Example of a credit application business 
process is shown in Fig. 1 . For space limitation we only show 
the control flow of the process. The process is modeled using 
Petri-Nets [6]. It begins with the recording of the application 
where the client expresses an interest in acquiring credit. 
This stage includes the presentation of the application, and 
the required documents to the organization for verification. 
This is followed by an analysis or study of the credit 
application to decide whether to accept/ reject the credit. 
The client is notified in case of rejection. In case of acceptance, 
the credit is disbursed to the client by either a credit transfer 
to a bank account or by check. 

III. Review of Techniques Towards Automatic BPR 

A BPR project starts when an organization is faced with 
the need to change its business process to make 
improvements in its quality, cost, service, lead-times, 
outcomes or flexibility. Usually, BPR projects are carried out 
by setting up workshops within the organization to think of 
alternatives to the business process. Consultants, employers, 
managers and specialties participate in these workshops to 
make process redesigns. Some Software tools may be used 
within these workshops to aid the redesign process. However, 
the identification of the problem areas and the opportunities 
of change are totally determined by the workshop group. As 
a result to this manual approach, the outcome redesigns are 
often subjective and non sustainable. This is because it is 
strongly influenced by the individual expertise of the 
workshop group and may not suit the actual case of the 
organization under study. 

To make process redesigns that actually succeed on the 
long term in improving the business process, the research in 
BPR was recently directed towards making automatic 
business process redesigns. In [7], a research project was 
proposed to address the lack of tools in industry with 
"intelligent" capabilities to suggest favorable alternatives to 
an existing process design. To develop such an "intelligent" 
redesign tool, the project proposed the idea of making 
evolutionary, local updates to an existing workflow design to 
gradually improve its performance. In this evolutionary 

©2011 ACEEE 

rX)1: 01.IJCOM.02.03. 42 

approach, the existing process is taken as a starting point 
and is refined on the basis of what is called "redesign best 
practices" [8]. Aredesign best practice describes a well-tried 
way to remove a particular problem from a process to improve 
its performance. An Example of a best practice is: Eliminating 
unnecessary tasks from a process (the tasks with no added 
value for customers). In [8], an extensive literature survey 
has taken place to collect all best practices for evolutionary 
process improvement. 

Towards developing this "intelligent" tool and based on 
the aforementioned evolutionary approach for BPR, a new 
technique to find process design alternatives was proposed 
in [9]. In this technique, a business process first is put in a 
formal process definition defined by the authors called Proto 
Net. A set of process measures are then calculated on the 
process (Ex: Level-of-Control, which is the percentage of 
control/decision tasks). The authors specified 18 different 
process measures to be calculated on the process design 
under study. The calculated measures are then compared 
against a set of condition statements that when evaluated to 
true a "redesign" best practice is selected to be applied on 
the process model (Ex: Apply Task Elimination if level of 
Control >0.2). Cutoff values for condition statements were 
determined by the authors' expertise in the field. 

Although this technique suggests the use of some best 
practices in the process model (the ones that their condition 
statements evaluated to true), it didn't specify exactly how 
these practices will be applied. As pointed out in [10], a 
redesign best practice just provides directions on how the 
redesign should be performed. When we look at the 
parallelism best practice, for instance, it is suggested that the 
redesign should have more tasks in parallel than the original 
process. But it does not tell us to put tasks A, B and of 
process X in parallel. In [ 10], four exact transformations were 
suggested to be applied on selected process parts of the 
process model to produce different redesigns. The input 
process model is assumed to be a Petri-net with some 
extensions like data dependencies and roles. 
The proposed transformations are: 

Unfolding of tasks, in which aggregated tasks (upper level 
tasks that are modeled in a detailed sub-process) are split up 
into several smaller tasks. 

Parallel Transformation, in which tasks that do not have 
data dependencies are executed in parallel. 
Sequence Transformation, in which all parallel paths in a 
model are transformed into sequence path, provided that the 
output sequence will have the lowest throughput time and 
will contain no errors related to data dependencies. 
Merging transformation, in which a task cluster executed by 


ACEEE Int. J. on Communication, Vol. 02, No. 03, Nov 201 1 

the same role is merged into one aggregated task. For each of 
the above transformations, the authors specified some formal 
characteristics that the selected process part 
should have in order to be eligible for the transformation (Ex: 
A selected part for the parallel transformation must not have 
a selective route to prevent contradictions with the selection's 
original purpose). They specified also how exactly the trans- 
formed part will be replaced in the Petri net model (Ex: Re- 
moving unnecessary edges/tasks). However, still their tech- 
nique doesn't allow the automatic selection of the process 
part to be altered or the automatic selection of the transfor- 
mation that produces the best result. 

In [ 1 1 ] , an algorithm called the Boolean Verification Algo- 
rithm (BVA) was presented for the optimization of workflows. 
On the contrary to the techniques mentioned before, this 
approach does not focus on applying best practices to the 
process design. It just focuses on finding the maximum 
parallelization for a design to reduce the overall execution 
time of the process. For this purpose, BVA uses a method 
called the if-con version. The main idea of this method is to 
assign Boolean activation conditions to workflow tasks based 
on their control flow dependence. While scanning the pro- 
cess model from the start task , BVA assigns Boolean control 
parameters on different branches and choice nodes (CI, 
C2, . . .Cn) and forms a Boolean activation condition for each 
task (Ex: !C1 & C2 v CI). Tasks are then checked for their 
control flow dependencies by analyzing their activations 
conditions and tasks with no control or data dependencies 
are parallelized. 

Although this algorithm promises to ensure a full 
parallelization of a business process, it still doesn't relate the 
reality to the design. In other words, it focuses only on the 
parallelization of tasks while in some cases, a sequential pro- 
cess may be perceived as a simpler process by employees 
and clients. Since the order of the tasks is fixed in sequential 
constructs, the execution of the process is done in the most 
logical way hence reducing errors. Furthermore, the synchro- 
nization that is required after the execution of tasks in parallel 
is not necessary in sequential processes. 

Let us note now that all the techniques that we men- 
tioned above don't satisfactorily support the automatic pro- 
duction of process redesigns. Although they provide guide- 
lines on how to apply different transformations on the pro- 
cess model, none of them supports either the automatic se- 
lection of the process part to redesign or the best transfor- 
mation to apply on it. 

TABLE I. A Process Log 

Cass Identifier 

Event Uenttt-Ler 

Case 1 

Task A 

Case 2 

Task A 

Case 3 

Task A 

Case 3 


Case 1 


Case 1 


Case 2 

Task C 

Case 2 


Case 3 


This is due to the lack of knowledge on what causes the low 
performance within the process. In the next sections, we will 
show how process mining can be used to enrich the business 
process model with the required knowledge to satisfactory 
allow automatic process transformations. 

IV Business Process Mining 

A. Introduction 

Process Mining allows for the extraction and the analysis 
of information from event logs recorded in BPM-based 
systems. Example of event logs are: the audit trails of a 
workflow management system, the transaction logs of an 
enterprise resource planning system, or the electronic patient 
records in a hospital. The two main components of a record 
in any event log are: the event (task/activity) that was 
executed and an identification of the particular instance of 
the process within which the activity was executed (case). 
More information can also be available in the log (for example, 
the timestamp of the event, or the performer of the event, the 
data elements recorded with the event, etc.). 

To illustrate the concept of process mining, consider the 
log information recorded in Table I. This log contains 
information on three execution cases (1,2 and 3). Executed 
events are represented as tasks and are assumed to be 
recorded in order. When scanning the log, we can detect four 
different tasks in the process (Tasks A, B, C and D). One can 
see also that all cases starts by task A and all cases ends by 
task D. In two cases (1 and 3), task B follows task A in the 
execution. In case 3, task C follows task A. From this 
information, we can simply draw, using Petri Nets, the process 
model that corresponds to this log as in fig. 2. 

©2011 ACEEE 

Figure 2. A process model corresponding to the log 

B. Process Mining Algorithms 

Over the last decade, many process mining algorithms 
has been developed to find process models that successfully 
mimics the behavior registered in the logs. In [12], a control 
flow mining algorithm called the alpha algorithm was 
presented. The alpha algorithm assumes that there is no noise 
(logs containing exceptions) in the data and that the log 
contains sufficient information about the workflow (no paths 
exist with low probability that prevents them from being 
detected). The alpha algorithm scans the log and looks for 
causal relations ( a relation between two tasks A and B such 
that B is directly followed by A in a log trace and A is never 
followed by B). These causal relations are then represented 
in a Petri-net model describing the output process. 


ACEEE Int. J. on Communication, Vol. 02, No. 03 , Nov 20 1 1 

To address the issue of noise existing in the log, an 
algorithm called the Heuristic Miner was presented in [13]. 
The Heuristic miner follows the alpha algorithm in finding 
causal relations between tasks. However, it calculates 
frequencies for the occurrence of each task and for the relation 
between tasks. Cutoff values are then used to eliminate tasks/ 
relations that are a result of noise/ un-complete executions. 
Because in some application areas, the resulting models after 
process mining become very difficult to understand 
(Spaghetti-Like), many process mining algorithms based on 
clustering was introduced to eliminate unnecessary 
information from the output models. The Fuzzy miner [14] for 
example starts by drawing a very complicated graph with all 
causal relations found in the log. It then simplifies the graph 
by creating clusters of nodes. Clusters are created by 
aggregating low significant nodes with their highly correlated 
neighbor nodes. Different significance and correlation 
matrices can be used for different application purposes. The 
lion's share of efforts in process mining has been for 
discovering control flow models resulting in many other 
models other than the ones mentioned above. However, 
process mining can also be used to mine much other useful 
information. As mentioned before, the event logs not only 
record information about the different cases and the different 
tasks. It also records information on the role who executed 
these tasks, the input and output attribute values to and 
from each task and the execution start and end time of tasks. 
This information can be used to mine the relations between 
the different roles creating an organizational model. It can 
also be used to analyze the information flow between the 
different roles, the interactions between the co-workers, the 
decision points in the models and the performance of 
executions [15], [16]. 

C. Process Mining Application in BPR 

Process mining has been applied in a variety of 
organizations covering many application domains. In [17], 
process mining was used to analyze the test process in 
ASML. ASML makes so-called wafer scanners that are used 
to manufacture processors in devices ranging from mobile 
phones to desktop computers. Wafer scanners are really 
complex machines that use a photographic process to image 
nanometric circuit patterns onto a silicon wafer. The testing 
of the manufactured wafer scanners is a time-consuming 
process. So, the goal of the analysis was to reduce the testing 

Each wafer scanner in the ASML factory produces a log 
of the software tests that are executed on it. Process mining 
was used to visualize the actual flow of the test process and 
confront this visualization with the idealized view of the tests 
according to engineers. It was found that as soon as one test 
fails, a fix is made to the scanner and all other tests are put on 
hold (idle time) and often after the fix is made, some tests are 
re-executed again. Visualizing this loop-backs caused by some 
tests gave the engineers a useful view on what was causing 
the time loss in the test process. Hence, allowed them to 
make changes to the test process to reduce the time 

©2011 ACEEE 

(for example, execute some tests at earlier phases). Healthcare 
is another famous application domain for process mining. 
The applicability of process mining in healthcare was 
demonstrated using a real case of a gynecological oncology 
process in the AMC hospital in the Netherlands [18]. The log 
data contained information about a representative group of 
627 gynecological oncology patients. The goal of using 
process mining was to discover the care paths followed by 
individual patients and whether certain procedures are 
followed or not. After applying process mining techniques, 
many useful results became visible to the people at the 
hospital. For Example, it was found that patients who undergo 
several chemotherapy sessions often need to visit the 
dietician. This was not immediately clear to everyone and 
illustrates the value of creating transparency using process 

The above two mentioned projects were implemented with 
the process mining tool named ProM [19]. ProM contains 
more than 250 plug-ins that implement different process 
mining algorithms. However, it is not clear how to use ProM 
in process redesign projects. In the above two projects, the 
authors used different plug-ins but viewed each plug-in result 
alone. Although ProM allows the results from some algorithms 
to be integrated in a Colored Petri Net (CPN) that support 
analysis and simulation, there was no guidance from ProM 
on how to improve the business processes. Instead, the 
researchers concluded the redesign ideas from viewing the 
simulated models, i.e. It is hard to make process redesign 
using process mining a repeatable service. 

V Proposed Software Framework for Automatic bpr 

A. Introduction 

Based on the discussion in the previous sections, we will 
now focus on two phases in BPR that, up to now, are done 
manually by the designers: 

The designer of the new process manually selects 
the process part to be redesigned from the old process model. 
He also decides what change to be made on the selected part. 
When using process mining for BPR, experts/ 
researchers in process mining determine which process 
mining algorithm to use and results after modeling do not 
suggest redesign ideas. 

For the above two points, we present our view on a 
software that automatically outputs a specific redesign to a 
business process using its recorded log information as input. 
In Fig. 3, the framework for this software is presented. The 
proposed software framework is composed of two main parts, 
a business process miner and a redesign engine. A Business 
Process miner is responsible simply for applying process 
mining techniques on the input process log to gather 
information that will aid the redesign process. The Redesign 
engine is responsible of generating new redesigns based on 
both process mining results and knowledge of the redesign 
best practices. Next, we illustrate both components in more 


ACEEE Int. J. on Communication, Vol. 02, No. 03, Nov 201 1 

Business Process Miner 


Process Miner I 


Process Miner 2 


Process Miner 3 


Redesign Engine 

Malfunction Part 


C J3 

— ► 


— ► 


I __J 


RedesiEn Generator 




1 Redesigns Evaluator 

i p Best redesign 

Figure 3. A Software Framework for Automatic Business Process Redesign 

B. The Business Process Miner Component 

In the business process miner, a proper selection of 
process mining algorithms is applied on the event logs to 
produce different model types. All the results gathered are 
then stored in a process mining results database. The choice 
of which process mining algorithms to apply on the log will 
be determined in a log inspection phase using some parametric 
characteristics from the log (for example, a log which contains 
a large no of tasks such that mining all these tasks will produce 
a spaghetti like model will be mined using the fuzzy miner). If 
role information is available in the log, the log will be mined 
to produce organizational and social network models. This 
role analysis is important since in some cases, the malfunction 
in a process design is mainly because of bad organizational 
or social structures. The log will be mined also to produce 
performance data like the throughput time of cases, the 
slowest tasks, the delays before tasks execution, the 
resources utilizations, . . .etc. 

C. The Redesign Engine Component 

Using results gathered from the business process miner, 
the redesign engine starts by determining the malfunction 
part in the process design (a certain component in the process 
where the mining results show that it somehow causes low 
performance). This malfunction part selector is the key 
element in this proposed software as it integrates the results 
from all the process mining algorithms to come out with 
conclusions on what causes the low performance of the 
process (For example, a specific path in the control flow model, 
a bad organization structure, etc. . .) . It is important to note 
also that, to select the problem areas in the business process, 
the targets for the redesign must be specifically input to the 
software in order. Targets can be for example, lowering cost, 
increasing quality, increasing flexibility, etc... The order of 
targets is important because, in some times targets contradict. 
For example, increasing the quality in some processes may 
result in increasing the cost and so on. To find the changes 
to apply on the selected process part, we suggest the use of 
©2011 ACEEE 

redesign best practices. Therefore, the proposed software 
must contain a database of redesign best practices that 
contains, for each best practice, an execution rule that shows: 
The conditions that a process part must match in 
order to be eligible to apply the best practice on, 
And, the process transformation that will be applied 
if these conditions are true. 
Matching the selected process part against the conditions in 
the best practices data base allows the redesign engine to 
find what transformations to apply on it. The selected part is 
then transformed and integrated with the process model in a 
redesign generator element. Since different combinations of 
transformations may applied to the same process part (more 
than one best practice rule evaluate to true), different 
redesigns to the process model may be generated. Hence, 
they are saved in a redesigns database. Moreover, if there 
are more process parts that need to be changed, a redesign 
loopback starts again by selecting another process part to 
change. The generated redesigns are then evaluated for the 
selection of the best redesign. The evaluation will be based 
on simulating the different redesigns using data from the 
logs (for example, the arrival time of different cases, the routing 
probabilities of different paths in the model, the response 
time from certain roles, . . . etc). 

VI. Conclusions 

In this paper we focused on how to automatically redesign 
business processes in order to increase its performance. We 
showed that current redesign methodologies let the designer 
choose the process part to be redesigned and also choose 
the proper change to apply on it. To allow automatic process 
redesign, process mining can be used. We briefly presented 
the concept of process mining and we showed that although 
a lot of process mining algorithms exist and some were already 
used in redesign projects, it is still not clear how to make 
process redesign using process mining a repeatable service. 
For this reason we presented a framework of a software that 



ACEEE Int. J. on Communication, Vol. 02, No. 03, Nov 201 1 

automatically produces a redesigned business process model 
using event logs from the old system execution. The basic 
idea of this software is to integrate the results after using a 
proper selection of process mining algorithms, and then use 
these results to select a malfunction process part and 
transform it using redesign best practices. 
The proposed framework provides guidelines on how to build 
the redesign software. To actually build this software, we 
plan to work on the following points: 

The different parameters and parameter values that 
determine the process mining algorithms to use. 

How to integrate the different results from process 
mining to find the process part to redesign. 

The rules that guide the use of a specific best 
practice (transformation) on a process part. 

The log analysis parameters that will be used for 
redesigns simulation. 


The authors would like to acknowledge that the work for 
this paper was partly funded by the Qatar Foundation for 
Education, Science and Community Development. The 
statements made herein are solely the responsibility of the 
authors and do not reflect any official position by the Qatar 
Foundation or Carnegie Mellon University. 


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©2011 ACEEE